Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations540
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory67.6 KiB
Average record size in memory128.2 B

Variable types

Numeric11
Categorical5

Alerts

Aggression is highly overall correlated with Agitation and 11 other fieldsHigh correlation
Agitation is highly overall correlated with Aggression and 11 other fieldsHigh correlation
Appetite is highly overall correlated with Aggression and 12 other fieldsHigh correlation
Concentration is highly overall correlated with Aggression and 12 other fieldsHigh correlation
Fatigue is highly overall correlated with Aggression and 11 other fieldsHigh correlation
Hopelessness is highly overall correlated with Aggression and 11 other fieldsHigh correlation
Interest is highly overall correlated with Aggression and 12 other fieldsHigh correlation
Low Energy is highly overall correlated with Aggression and 11 other fieldsHigh correlation
Panic Attacks is highly overall correlated with Aggression and 11 other fieldsHigh correlation
Restlessness is highly overall correlated with Aggression and 11 other fieldsHigh correlation
Sleep is highly overall correlated with Appetite and 3 other fieldsHigh correlation
Sleep Disturbance is highly overall correlated with Aggression and 12 other fieldsHigh correlation
Suicidal Ideation is highly overall correlated with Aggression and 11 other fieldsHigh correlation
Worthlessness is highly overall correlated with Aggression and 11 other fieldsHigh correlation
Number is uniformly distributed Uniform
Number has unique values Unique

Reproduction

Analysis started2024-10-20 11:03:20.180256
Analysis finished2024-10-20 11:03:25.504855
Duration5.32 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

Number
Real number (ℝ)

Uniform  Unique 

Distinct540
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean270.5
Minimum1
Maximum540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-10-20T18:03:25.545176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile27.95
Q1135.75
median270.5
Q3405.25
95-th percentile513.05
Maximum540
Range539
Interquartile range (IQR)269.5

Descriptive statistics

Standard deviation156.02884
Coefficient of variation (CV)0.57681643
Kurtosis-1.2
Mean270.5
Median Absolute Deviation (MAD)135
Skewness0
Sum146070
Variance24345
MonotonicityStrictly increasing
2024-10-20T18:03:25.602910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
356 1
 
0.2%
370 1
 
0.2%
369 1
 
0.2%
368 1
 
0.2%
367 1
 
0.2%
366 1
 
0.2%
365 1
 
0.2%
364 1
 
0.2%
363 1
 
0.2%
Other values (530) 530
98.1%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
540 1
0.2%
539 1
0.2%
538 1
0.2%
537 1
0.2%
536 1
0.2%
535 1
0.2%
534 1
0.2%
533 1
0.2%
532 1
0.2%
531 1
0.2%

Sleep
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.912963
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-10-20T18:03:25.650690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.7384174
Coefficient of variation (CV)0.59678665
Kurtosis-1.5772373
Mean2.912963
Median Absolute Deviation (MAD)1
Skewness0.30649058
Sum1573
Variance3.0220951
MonotonicityNot monotonic
2024-10-20T18:03:25.692202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 170
31.5%
1 157
29.1%
2 148
27.4%
3 24
 
4.4%
4 24
 
4.4%
6 17
 
3.1%
ValueCountFrequency (%)
1 157
29.1%
2 148
27.4%
3 24
 
4.4%
4 24
 
4.4%
5 170
31.5%
6 17
 
3.1%
ValueCountFrequency (%)
6 17
 
3.1%
5 170
31.5%
4 24
 
4.4%
3 24
 
4.4%
2 148
27.4%
1 157
29.1%

Appetite
Categorical

High correlation 

Distinct5
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
5
172 
1
170 
2
148 
3
26 
4
24 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters540
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row5
3rd row2
4th row1
5th row5

Common Values

ValueCountFrequency (%)
5 172
31.9%
1 170
31.5%
2 148
27.4%
3 26
 
4.8%
4 24
 
4.4%

Length

2024-10-20T18:03:25.737318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-20T18:03:25.783260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
5 172
31.9%
1 170
31.5%
2 148
27.4%
3 26
 
4.8%
4 24
 
4.4%

Most occurring characters

ValueCountFrequency (%)
5 172
31.9%
1 170
31.5%
2 148
27.4%
3 26
 
4.8%
4 24
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 540
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 172
31.9%
1 170
31.5%
2 148
27.4%
3 26
 
4.8%
4 24
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 540
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 172
31.9%
1 170
31.5%
2 148
27.4%
3 26
 
4.8%
4 24
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 540
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 172
31.9%
1 170
31.5%
2 148
27.4%
3 26
 
4.8%
4 24
 
4.4%

Interest
Categorical

High correlation 

Distinct5
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
5
174 
1
170 
2
148 
3
24 
4
24 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters540
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row5
3rd row2
4th row1
5th row5

Common Values

ValueCountFrequency (%)
5 174
32.2%
1 170
31.5%
2 148
27.4%
3 24
 
4.4%
4 24
 
4.4%

Length

2024-10-20T18:03:25.828712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-20T18:03:25.868349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
5 174
32.2%
1 170
31.5%
2 148
27.4%
3 24
 
4.4%
4 24
 
4.4%

Most occurring characters

ValueCountFrequency (%)
5 174
32.2%
1 170
31.5%
2 148
27.4%
3 24
 
4.4%
4 24
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 540
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 174
32.2%
1 170
31.5%
2 148
27.4%
3 24
 
4.4%
4 24
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 540
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 174
32.2%
1 170
31.5%
2 148
27.4%
3 24
 
4.4%
4 24
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 540
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 174
32.2%
1 170
31.5%
2 148
27.4%
3 24
 
4.4%
4 24
 
4.4%

Fatigue
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9648148
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-10-20T18:03:25.908271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.7274019
Coefficient of variation (CV)0.582634
Kurtosis-1.5940597
Mean2.9648148
Median Absolute Deviation (MAD)1
Skewness0.26474219
Sum1601
Variance2.9839174
MonotonicityNot monotonic
2024-10-20T18:03:25.949451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 174
32.2%
2 154
28.5%
1 145
26.9%
4 26
 
4.8%
3 24
 
4.4%
6 17
 
3.1%
ValueCountFrequency (%)
1 145
26.9%
2 154
28.5%
3 24
 
4.4%
4 26
 
4.8%
5 174
32.2%
6 17
 
3.1%
ValueCountFrequency (%)
6 17
 
3.1%
5 174
32.2%
4 26
 
4.8%
3 24
 
4.4%
2 154
28.5%
1 145
26.9%

Worthlessness
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9574074
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-10-20T18:03:25.989278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.7400768
Coefficient of variation (CV)0.58837911
Kurtosis-1.6068773
Mean2.9574074
Median Absolute Deviation (MAD)1
Skewness0.26281118
Sum1597
Variance3.0278671
MonotonicityNot monotonic
2024-10-20T18:03:26.031587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 176
32.6%
1 151
28.0%
2 148
27.4%
3 24
 
4.4%
4 24
 
4.4%
6 17
 
3.1%
ValueCountFrequency (%)
1 151
28.0%
2 148
27.4%
3 24
 
4.4%
4 24
 
4.4%
5 176
32.6%
6 17
 
3.1%
ValueCountFrequency (%)
6 17
 
3.1%
5 176
32.6%
4 24
 
4.4%
3 24
 
4.4%
2 148
27.4%
1 151
28.0%

Concentration
Categorical

High correlation 

Distinct5
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
5
172 
1
168 
2
152 
3
24 
4
24 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters540
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row5
3rd row2
4th row1
5th row5

Common Values

ValueCountFrequency (%)
5 172
31.9%
1 168
31.1%
2 152
28.1%
3 24
 
4.4%
4 24
 
4.4%

Length

2024-10-20T18:03:26.077845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-20T18:03:26.118091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
5 172
31.9%
1 168
31.1%
2 152
28.1%
3 24
 
4.4%
4 24
 
4.4%

Most occurring characters

ValueCountFrequency (%)
5 172
31.9%
1 168
31.1%
2 152
28.1%
3 24
 
4.4%
4 24
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 540
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 172
31.9%
1 168
31.1%
2 152
28.1%
3 24
 
4.4%
4 24
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 540
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 172
31.9%
1 168
31.1%
2 152
28.1%
3 24
 
4.4%
4 24
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 540
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 172
31.9%
1 168
31.1%
2 152
28.1%
3 24
 
4.4%
4 24
 
4.4%

Agitation
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9685185
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-10-20T18:03:26.160618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.7199392
Coefficient of variation (CV)0.57939311
Kurtosis-1.584113
Mean2.9685185
Median Absolute Deviation (MAD)1
Skewness0.27069698
Sum1603
Variance2.9581908
MonotonicityNot monotonic
2024-10-20T18:03:26.203511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 174
32.2%
2 158
29.3%
1 141
26.1%
3 26
 
4.8%
4 24
 
4.4%
6 17
 
3.1%
ValueCountFrequency (%)
1 141
26.1%
2 158
29.3%
3 26
 
4.8%
4 24
 
4.4%
5 174
32.2%
6 17
 
3.1%
ValueCountFrequency (%)
6 17
 
3.1%
5 174
32.2%
4 24
 
4.4%
3 26
 
4.8%
2 158
29.3%
1 141
26.1%

Suicidal Ideation
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9648148
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-10-20T18:03:26.243850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.7338341
Coefficient of variation (CV)0.58480352
Kurtosis-1.6025804
Mean2.9648148
Median Absolute Deviation (MAD)1
Skewness0.26225921
Sum1601
Variance3.0061809
MonotonicityNot monotonic
2024-10-20T18:03:26.286722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 176
32.6%
2 152
28.1%
1 147
27.2%
3 24
 
4.4%
4 24
 
4.4%
6 17
 
3.1%
ValueCountFrequency (%)
1 147
27.2%
2 152
28.1%
3 24
 
4.4%
4 24
 
4.4%
5 176
32.6%
6 17
 
3.1%
ValueCountFrequency (%)
6 17
 
3.1%
5 176
32.6%
4 24
 
4.4%
3 24
 
4.4%
2 152
28.1%
1 147
27.2%

Sleep Disturbance
Categorical

High correlation 

Distinct5
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
5
172 
2
162 
1
156 
3
26 
4
24 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters540
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row5
3rd row2
4th row1
5th row5

Common Values

ValueCountFrequency (%)
5 172
31.9%
2 162
30.0%
1 156
28.9%
3 26
 
4.8%
4 24
 
4.4%

Length

2024-10-20T18:03:26.331662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-20T18:03:26.372526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
5 172
31.9%
2 162
30.0%
1 156
28.9%
3 26
 
4.8%
4 24
 
4.4%

Most occurring characters

ValueCountFrequency (%)
5 172
31.9%
2 162
30.0%
1 156
28.9%
3 26
 
4.8%
4 24
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 540
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 172
31.9%
2 162
30.0%
1 156
28.9%
3 26
 
4.8%
4 24
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 540
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 172
31.9%
2 162
30.0%
1 156
28.9%
3 26
 
4.8%
4 24
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 540
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 172
31.9%
2 162
30.0%
1 156
28.9%
3 26
 
4.8%
4 24
 
4.4%

Aggression
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9796296
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-10-20T18:03:26.412838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.7211852
Coefficient of variation (CV)0.57765072
Kurtosis-1.5940802
Mean2.9796296
Median Absolute Deviation (MAD)1
Skewness0.26170246
Sum1609
Variance2.9624785
MonotonicityNot monotonic
2024-10-20T18:03:26.455861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 176
32.6%
2 160
29.6%
1 139
25.7%
3 24
 
4.4%
4 24
 
4.4%
6 17
 
3.1%
ValueCountFrequency (%)
1 139
25.7%
2 160
29.6%
3 24
 
4.4%
4 24
 
4.4%
5 176
32.6%
6 17
 
3.1%
ValueCountFrequency (%)
6 17
 
3.1%
5 176
32.6%
4 24
 
4.4%
3 24
 
4.4%
2 160
29.6%
1 139
25.7%

Panic Attacks
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.987037
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-10-20T18:03:26.543286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q35
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7082737
Coefficient of variation (CV)0.57189572
Kurtosis-1.5812612
Mean2.987037
Median Absolute Deviation (MAD)1
Skewness0.26453784
Sum1613
Variance2.918199
MonotonicityNot monotonic
2024-10-20T18:03:26.585226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 174
32.2%
2 166
30.7%
1 133
24.6%
4 26
 
4.8%
3 24
 
4.4%
6 17
 
3.1%
ValueCountFrequency (%)
1 133
24.6%
2 166
30.7%
3 24
 
4.4%
4 26
 
4.8%
5 174
32.2%
6 17
 
3.1%
ValueCountFrequency (%)
6 17
 
3.1%
5 174
32.2%
4 26
 
4.8%
3 24
 
4.4%
2 166
30.7%
1 133
24.6%

Hopelessness
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9648148
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-10-20T18:03:26.626512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.7231004
Coefficient of variation (CV)0.58118316
Kurtosis-1.5862634
Mean2.9648148
Median Absolute Deviation (MAD)1
Skewness0.27079034
Sum1601
Variance2.9690751
MonotonicityNot monotonic
2024-10-20T18:03:26.669277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 174
32.2%
2 156
28.9%
1 143
26.5%
3 26
 
4.8%
4 24
 
4.4%
6 17
 
3.1%
ValueCountFrequency (%)
1 143
26.5%
2 156
28.9%
3 26
 
4.8%
4 24
 
4.4%
5 174
32.2%
6 17
 
3.1%
ValueCountFrequency (%)
6 17
 
3.1%
5 174
32.2%
4 24
 
4.4%
3 26
 
4.8%
2 156
28.9%
1 143
26.5%

Restlessness
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9648148
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-10-20T18:03:26.711084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.7338341
Coefficient of variation (CV)0.58480352
Kurtosis-1.6025804
Mean2.9648148
Median Absolute Deviation (MAD)1
Skewness0.26225921
Sum1601
Variance3.0061809
MonotonicityNot monotonic
2024-10-20T18:03:26.761603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 176
32.6%
2 152
28.1%
1 147
27.2%
3 24
 
4.4%
4 24
 
4.4%
6 17
 
3.1%
ValueCountFrequency (%)
1 147
27.2%
2 152
28.1%
3 24
 
4.4%
4 24
 
4.4%
5 176
32.6%
6 17
 
3.1%
ValueCountFrequency (%)
6 17
 
3.1%
5 176
32.6%
4 24
 
4.4%
3 24
 
4.4%
2 152
28.1%
1 147
27.2%

Low Energy
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9240741
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-10-20T18:03:26.812869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.7271632
Coefficient of variation (CV)0.59067014
Kurtosis-1.5870265
Mean2.9240741
Median Absolute Deviation (MAD)1
Skewness0.29329114
Sum1579
Variance2.9830928
MonotonicityNot monotonic
2024-10-20T18:03:26.860341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 173
32.0%
1 152
28.1%
2 152
28.1%
3 24
 
4.4%
4 24
 
4.4%
6 15
 
2.8%
ValueCountFrequency (%)
1 152
28.1%
2 152
28.1%
3 24
 
4.4%
4 24
 
4.4%
5 173
32.0%
6 15
 
2.8%
ValueCountFrequency (%)
6 15
 
2.8%
5 173
32.0%
4 24
 
4.4%
3 24
 
4.4%
2 152
28.1%
1 152
28.1%

Depression State
Categorical

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
No depression
174 
Mild
128 
Moderate
120 
Severe
118 

Length

Max length13
Median length8
Mean length8.2259259
Min length4

Characters and Unicode

Total characters4442
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMild
2nd rowModerate
3rd rowSevere
4th rowNo depression
5th rowModerate

Common Values

ValueCountFrequency (%)
No depression 174
32.2%
Mild 128
23.7%
Moderate 120
22.2%
Severe 118
21.9%

Length

2024-10-20T18:03:26.909395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-20T18:03:26.952184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 174
24.4%
depression 174
24.4%
mild 128
17.9%
moderate 120
16.8%
severe 118
16.5%

Most occurring characters

ValueCountFrequency (%)
e 942
21.2%
o 468
10.5%
d 422
9.5%
r 412
9.3%
s 348
 
7.8%
i 302
 
6.8%
M 248
 
5.6%
N 174
 
3.9%
174
 
3.9%
p 174
 
3.9%
Other values (6) 778
17.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4442
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 942
21.2%
o 468
10.5%
d 422
9.5%
r 412
9.3%
s 348
 
7.8%
i 302
 
6.8%
M 248
 
5.6%
N 174
 
3.9%
174
 
3.9%
p 174
 
3.9%
Other values (6) 778
17.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4442
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 942
21.2%
o 468
10.5%
d 422
9.5%
r 412
9.3%
s 348
 
7.8%
i 302
 
6.8%
M 248
 
5.6%
N 174
 
3.9%
174
 
3.9%
p 174
 
3.9%
Other values (6) 778
17.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4442
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 942
21.2%
o 468
10.5%
d 422
9.5%
r 412
9.3%
s 348
 
7.8%
i 302
 
6.8%
M 248
 
5.6%
N 174
 
3.9%
174
 
3.9%
p 174
 
3.9%
Other values (6) 778
17.5%

Interactions

2024-10-20T18:03:24.887060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:20.404049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:20.986109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:21.411636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:21.818342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:22.286257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:22.691904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:23.112241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:23.562017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:24.068546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:24.475690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:24.923870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:20.493661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:21.025923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:21.449376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:21.855076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:22.323146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:22.729358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:23.151039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:23.600479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:24.105731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:24.512437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:24.960734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:20.552587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:21.066991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:21.485735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:21.892504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:22.360449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:22.766894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:23.187242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:23.638562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:24.142874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:24.549993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:24.998560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:20.672974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:21.105202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:21.522715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:21.929874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:22.396476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:22.803614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:23.224587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:23.684580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:24.179147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:24.586355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:25.035308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:20.712335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:21.143843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:21.559551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:21.967228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:22.434210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:22.842076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:23.261158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:23.791303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:24.216812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:24.624032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:25.073086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:20.752675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:21.183208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:21.595949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:22.003884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:22.470156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:22.880710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:23.298630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:23.830629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:24.253701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:24.661136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:25.110069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:20.791410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:21.221108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:21.634021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:22.042324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:22.506482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:22.918132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:23.335250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:23.873400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:24.291696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:24.700938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:25.148023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:20.830845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:21.259343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:21.670393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:22.087908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:22.543732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:22.958917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:23.414535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:23.913078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:24.328079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:24.739280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:25.184617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:20.870940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:21.297470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:21.707418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:22.129227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:22.580688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:22.996148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:23.451712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:23.957625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:24.365723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:24.775788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:25.221939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:20.909666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:21.336082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:21.744286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:22.212183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:22.618474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:23.034726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:23.488128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:23.994194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:24.401842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:24.813403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:25.304255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:20.948016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:21.374102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:21.782177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:22.249095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:22.654834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:23.073614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:23.525117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:24.031612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:24.438376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T18:03:24.849570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-20T18:03:26.993089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AggressionAgitationAppetiteConcentrationDepression StateFatigueHopelessnessInterestLow EnergyNumberPanic AttacksRestlessnessSleepSleep DisturbanceSuicidal IdeationWorthlessness
Aggression1.0000.9860.8970.9030.3540.9900.9770.9130.8640.0190.9860.993-0.1940.8770.9820.989
Agitation0.9861.0000.9110.8940.3440.9820.9910.9060.8720.0170.9890.993-0.1890.8920.9820.986
Appetite0.8970.9111.0000.9830.3510.8970.9140.9760.8540.2970.8780.9090.9450.9790.9090.915
Concentration0.9030.8940.9831.0000.3620.8990.8960.9790.8590.2990.8810.9150.9510.9620.9150.921
Depression State0.3540.3440.3510.3621.0000.3440.3420.3600.3380.0000.3440.3530.3590.3470.3550.354
Fatigue0.9900.9820.8970.8990.3441.0000.9870.9120.8690.0100.9860.986-0.1850.8760.9900.990
Hopelessness0.9770.9910.9140.8960.3420.9871.0000.9090.8680.0140.9870.984-0.1860.8940.9880.988
Interest0.9130.9060.9760.9790.3600.9120.9091.0000.8720.2970.8890.9250.9600.9560.9250.931
Low Energy0.8640.8720.8540.8590.3380.8690.8680.8721.000-0.0070.8590.872-0.1620.8360.8660.871
Number0.0190.0170.2970.2990.0000.0100.0140.297-0.0071.0000.0320.0040.0040.3060.005-0.003
Panic Attacks0.9860.9890.8780.8810.3440.9860.9870.8890.8590.0321.0000.983-0.2040.8580.9760.976
Restlessness0.9930.9930.9090.9150.3530.9860.9840.9250.8720.0040.9831.000-0.1850.8880.9890.993
Sleep-0.194-0.1890.9450.9510.359-0.185-0.1860.960-0.1620.004-0.204-0.1851.0000.926-0.185-0.180
Sleep Disturbance0.8770.8920.9790.9620.3470.8760.8940.9560.8360.3060.8580.8880.9261.0000.8880.895
Suicidal Ideation0.9820.9820.9090.9150.3550.9900.9880.9250.8660.0050.9760.989-0.1850.8881.0000.993
Worthlessness0.9890.9860.9150.9210.3540.9900.9880.9310.871-0.0030.9760.993-0.1800.8950.9931.000

Missing values

2024-10-20T18:03:25.361758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-20T18:03:25.455827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

NumberSleepAppetiteInterestFatigueWorthlessnessConcentrationAgitationSuicidal IdeationSleep DisturbanceAggressionPanic AttacksHopelessnessRestlessnessLow EnergyDepression State
0111155155155555Mild
1225511511511111Moderate
2352222222222222Severe
3411155155155555No depression
4525511511511111Moderate
5652222222222222Mild
6711155155155555No depression
7825511511511111Severe
8952222222222222Moderate
91061155155155555Mild
NumberSleepAppetiteInterestFatigueWorthlessnessConcentrationAgitationSuicidal IdeationSleep DisturbanceAggressionPanic AttacksHopelessnessRestlessnessLow EnergyDepression State
53053152222222222226Moderate
53153261155155155552No depression
53253325566566566665No depression
53353452222222222221Mild
53453511155155155552No depression
53553625511511511115Mild
53653752222222222221Severe
53753811155155155552No depression
53853925511511511111Severe
53954052222222222222Moderate